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Pandas Series.value_counts()

Pandas Series.value_counts() with What is Python Pandas, Reading Multiple Files, Null values, Multiple index, Application, Application Basics, Resampling, Plotting the data, Moving windows functions, Series, Read the file, Data operations, Filter Data etc.

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Pandas Series.value_counts()

The value_counts() function returns a Series that contain counts of unique values. It returns an object that will be in descending order so that its first element will be the most frequently-occurred element.

By default, it excludes NA values.

Syntax

Series.value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True)

Parameters

  • normalize: If it is true, then the returned object will contain the relative frequencies of the unique values.
  • sort: It sort by the values.
  • ascending: It sort in the ascending order.
  • bins: Rather than counting the values, it groups them into the half-open bins that provide convenience for the pd.cut, which only works with numeric data.
  • dropna: It does not include counts of NaN.

Returns

It returns the counted series.

Example1

import pandas as pd
import numpy as np
index = pd.Index([2, 1, 1, np.nan, 3])
index.value_counts()

Output

1.0    2
3.0    1
2.0    1
dtype: int64

Example2

import pandas as pd
import numpy as np
index = pd.Index([2, 1, 1, np.nan, 3])
a = pd.Series([2, 1, 1, np.nan, 3])
a.value_counts(normalize=True)

Output

1.0    0.50
3.0    0.25
2.0    0.25
dtype: float64

Example3

import pandas as pd
index = pd.Index([1, 3, 2, 2, 1, np.nan])
index.value_counts()
a = pd.Series([1, 3, 2, 2, 1, np.nan])
a.value_counts(bins=2)

Output

(0.997, 2.0]    4
(2.0, 3.0]        1
dtype: int64

Example4

import pandas as pd
index = pd.Index([1, 3, 2, 2, 1, np.nan])
index.value_counts()
a = pd.Series([1, 3, 2, 2, 1, np.nan])
a.value_counts(dropna=False)

Output

2.0     2
1.0     2
NaN   1
3.0     1
dtype: int64
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